code: def forward(self, x, edge_index, batch):
      xs = [x]
      for conv in self.convs:
          xs += [conv(xs[-1], edge_index).tanh()]
      x = torch.cat(xs[1:], dim=-1)
  
      # Global pooling.
      x = self.pool(x, batch)
      x = x.unsqueeze(1)  # [num_graphs, 1, k * hidden]
      x = self.conv1(x).relu()
      x = self.maxpool1d(x)
      x = self.conv2(x).relu()
      x = x.view(x.size(0), -1)  # [num_graphs, dense_dim]
  
      return self.mlp(x)
  
imports: import math
  import os.path
  import time
  from itertools import chain
  import numpy
  import torch
  import torch.nn.functional
  from scipy.sparse.csgraph import shortest_path
  from sklearn.metrics import roc_auc_score
  from torch.nn import BCEWithLogitsLoss, Conv1d, MaxPool1d, ModuleList
  from torch_geometric.data import Data, InMemoryDataset
  from torch_geometric.datasets import Planetoid
  from torch_geometric.loader import DataLoader
  from torch_geometric.nn import MLP, GCNConv, SortAggregation
  from torch_geometric.transforms import RandomLinkSplit
  from torch_geometric.utils import k_hop_subgraph, to_scipy_sparse_matrix
label_desc: Use image node_id 2 for calling the DGCNN obj's underlying member method code with example usage: obj.forward(x, edge_index, batch) and returns: self